Regularized GMM for Time‐varying Models With Applications to Asset Pricing

Abstract

We propose a regularized GMM approach to estimating time‐varying coefficient models via a ridge fusion penalty with a high‐dimensional set of moment conditions. RegGMM only requires a mild condition on the oscillations between consecutive parameter values, accommodating abrupt structural breaks and smooth changes throughout the sample period. RegGMM offers an alternative solution for estimating the time‐varying stochastic discount factor model when pricing U.S. equity cross‐sectional returns. Our time‐varying estimate paths for factor risk prices capture changing performance across multiple risk factors and depict potential regime‐switching scenarios. Finally, RegGMM demonstrates superior asset pricing and investment performance gains compared to alternative methods.This article is protected by copyright. All rights reserved